Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3813
Missing cells5894
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory639.1 B

Variable types

Categorical10
Text3
Numeric10

Alerts

area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 3 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
facing is highly overall correlated with property typeHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sq_ft is highly overall correlated with priceHigh correlation
property type is highly overall correlated with bedRoom and 3 other fieldsHigh correlation
servant room is highly overall correlated with super_built_up_areaHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%) Imbalance
super_built_up_area has 1920 (50.4%) missing values Missing
built_up_area has 2028 (53.2%) missing values Missing
carpet_area has 1886 (49.5%) missing values Missing
area is highly skewed (γ1 = 58.49483978) Skewed
built_up_area is highly skewed (γ1 = 41.74022428) Skewed
carpet_area is highly skewed (γ1 = 24.68711161) Skewed
floorNum has 134 (3.5%) zeros Zeros
luxary_score has 525 (13.8%) zeros Zeros

Reproduction

Analysis started2024-11-11 10:24:50.762241
Analysis finished2024-11-11 10:24:59.393874
Duration8.63 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

property type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size257.9 KiB
flat
2869 
house
944 

Length

Max length5
Median length4
Mean length4.2475741
Min length4

Characters and Unicode

Total characters16196
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhouse
2nd rowhouse
3rd rowhouse
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2869
75.2%
house 944
 
24.8%

Length

2024-11-11T15:54:59.447137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:54:59.523304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2869
75.2%
house 944
 
24.8%

Most occurring characters

ValueCountFrequency (%)
f 2869
17.7%
l 2869
17.7%
a 2869
17.7%
t 2869
17.7%
h 944
 
5.8%
o 944
 
5.8%
u 944
 
5.8%
s 944
 
5.8%
e 944
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16196
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2869
17.7%
l 2869
17.7%
a 2869
17.7%
t 2869
17.7%
h 944
 
5.8%
o 944
 
5.8%
u 944
 
5.8%
s 944
 
5.8%
e 944
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 16196
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2869
17.7%
l 2869
17.7%
a 2869
17.7%
t 2869
17.7%
h 944
 
5.8%
o 944
 
5.8%
u 944
 
5.8%
s 944
 
5.8%
e 944
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2869
17.7%
l 2869
17.7%
a 2869
17.7%
t 2869
17.7%
h 944
 
5.8%
o 944
 
5.8%
u 944
 
5.8%
s 944
 
5.8%
e 944
 
5.8%

society
Text

Distinct722
Distinct (%)18.9%
Missing1
Missing (%)< 0.1%
Memory size304.4 KiB
2024-11-11T15:54:59.755018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length41
Mean length16.767838
Min length1

Characters and Unicode

Total characters63919
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)8.9%

Sample

1st rowindependent
2nd rowindependent
3rd rowindependent
4th rowaipl zen residences
5th rowthe close north
ValueCountFrequency (%)
independent 563
 
5.7%
the 351
 
3.5%
dlf 222
 
2.2%
park 211
 
2.1%
city 169
 
1.7%
global 155
 
1.6%
emaar 154
 
1.6%
m3m 152
 
1.5%
signature 150
 
1.5%
heights 133
 
1.3%
Other values (814) 7659
77.2%
2024-11-11T15:55:00.107004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 7026
 
11.0%
6109
 
9.6%
a 6007
 
9.4%
n 4451
 
7.0%
r 4271
 
6.7%
i 3945
 
6.2%
t 3858
 
6.0%
s 3539
 
5.5%
l 2981
 
4.7%
o 2810
 
4.4%
Other values (31) 18922
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57234
89.5%
Space Separator 6109
 
9.6%
Decimal Number 550
 
0.9%
Other Punctuation 15
 
< 0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7026
12.3%
a 6007
 
10.5%
n 4451
 
7.8%
r 4271
 
7.5%
i 3945
 
6.9%
t 3858
 
6.7%
s 3539
 
6.2%
l 2981
 
5.2%
o 2810
 
4.9%
d 2653
 
4.6%
Other values (16) 15693
27.4%
Decimal Number
ValueCountFrequency (%)
3 211
38.4%
2 84
 
15.3%
1 83
 
15.1%
6 57
 
10.4%
8 32
 
5.8%
4 19
 
3.5%
5 19
 
3.5%
7 16
 
2.9%
0 15
 
2.7%
9 14
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 12
80.0%
/ 2
 
13.3%
. 1
 
6.7%
Space Separator
ValueCountFrequency (%)
6109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57234
89.5%
Common 6685
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7026
12.3%
a 6007
 
10.5%
n 4451
 
7.8%
r 4271
 
7.5%
i 3945
 
6.9%
t 3858
 
6.7%
s 3539
 
6.2%
l 2981
 
5.2%
o 2810
 
4.9%
d 2653
 
4.6%
Other values (16) 15693
27.4%
Common
ValueCountFrequency (%)
6109
91.4%
3 211
 
3.2%
2 84
 
1.3%
1 83
 
1.2%
6 57
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 19
 
0.3%
7 16
 
0.2%
0 15
 
0.2%
Other values (5) 40
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7026
 
11.0%
6109
 
9.6%
a 6007
 
9.4%
n 4451
 
7.0%
r 4271
 
6.7%
i 3945
 
6.2%
t 3858
 
6.0%
s 3539
 
5.5%
l 2981
 
4.7%
o 2810
 
4.4%
Other values (31) 18922
29.6%

sector
Text

Distinct103
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size275.9 KiB
2024-11-11T15:55:00.269836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.1014949
Min length8

Characters and Unicode

Total characters34704
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowsector 31
2nd rowsector 1
3rd rowsector 50
4th rowsector 70
5th rowsector 50
ValueCountFrequency (%)
sector 3813
50.0%
163 154
 
2.0%
37 122
 
1.6%
85 108
 
1.4%
102 108
 
1.4%
70 105
 
1.4%
92 100
 
1.3%
69 93
 
1.2%
90 88
 
1.2%
109 88
 
1.2%
Other values (94) 2847
37.3%
2024-11-11T15:55:00.655007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 3813
11.0%
e 3813
11.0%
c 3813
11.0%
t 3813
11.0%
o 3813
11.0%
r 3813
11.0%
3813
11.0%
1 1275
 
3.7%
6 905
 
2.6%
3 871
 
2.5%
Other values (8) 4962
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22882
65.9%
Decimal Number 8009
 
23.1%
Space Separator 3813
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1275
15.9%
6 905
11.3%
3 871
10.9%
8 851
10.6%
0 839
10.5%
9 797
10.0%
7 684
8.5%
2 678
8.5%
5 664
8.3%
4 445
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
s 3813
16.7%
e 3813
16.7%
c 3813
16.7%
t 3813
16.7%
o 3813
16.7%
r 3813
16.7%
a 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22882
65.9%
Common 11822
34.1%

Most frequent character per script

Common
ValueCountFrequency (%)
3813
32.3%
1 1275
 
10.8%
6 905
 
7.7%
3 871
 
7.4%
8 851
 
7.2%
0 839
 
7.1%
9 797
 
6.7%
7 684
 
5.8%
2 678
 
5.7%
5 664
 
5.6%
Latin
ValueCountFrequency (%)
s 3813
16.7%
e 3813
16.7%
c 3813
16.7%
t 3813
16.7%
o 3813
16.7%
r 3813
16.7%
a 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 3813
11.0%
e 3813
11.0%
c 3813
11.0%
t 3813
11.0%
o 3813
11.0%
r 3813
11.0%
3813
11.0%
1 1275
 
3.7%
6 905
 
2.6%
3 871
 
2.5%
Other values (8) 4962
14.3%

price
Real number (ℝ)

High correlation 

Distinct476
Distinct (%)12.5%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.4891697
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:00.757089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.91
median1.5
Q32.7
95-th percentile8.414
Maximum31.5
Range31.43
Interquartile range (IQR)1.79

Descriptive statistics

Standard deviation2.9431885
Coefficient of variation (CV)1.1823977
Kurtosis15.459362
Mean2.4891697
Median Absolute Deviation (MAD)0.72
Skewness3.3324322
Sum9443.91
Variance8.6623588
MonotonicityNot monotonic
2024-11-11T15:55:00.849923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 81
 
2.1%
1.5 67
 
1.8%
1.1 65
 
1.7%
0.9 65
 
1.7%
1.2 64
 
1.7%
1.4 61
 
1.6%
1.3 61
 
1.6%
0.95 56
 
1.5%
2 56
 
1.5%
1.6 51
 
1.3%
Other values (466) 3167
83.1%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.13 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.18 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 9
0.2%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sq_ft
Real number (ℝ)

High correlation 

Distinct2719
Distinct (%)71.7%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13897.771
Minimum2
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:00.941270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4666
Q16800
median9000
Q313769
95-th percentile33333
Maximum600000
Range599998
Interquartile range (IQR)6969

Descriptive statistics

Standard deviation22931.157
Coefficient of variation (CV)1.6499881
Kurtosis184.88436
Mean13897.771
Median Absolute Deviation (MAD)2788
Skewness11.207728
Sum52728145
Variance5.2583797 × 108
MonotonicityNot monotonic
2024-11-11T15:55:01.033294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 28
 
0.7%
8000 21
 
0.6%
5000 18
 
0.5%
11111 15
 
0.4%
12500 15
 
0.4%
8333 14
 
0.4%
22222 14
 
0.4%
6666 14
 
0.4%
6000 12
 
0.3%
7500 12
 
0.3%
Other values (2709) 3631
95.2%
(Missing) 19
 
0.5%
ValueCountFrequency (%)
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%
235376 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1345
Distinct (%)35.5%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean4760.4755
Minimum45
Maximum7250000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:01.125069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile500
Q11200
median1709
Q32290
95-th percentile4212.25
Maximum7250000
Range7249955
Interquartile range (IQR)1090

Descriptive statistics

Standard deviation119840.93
Coefficient of variation (CV)25.17415
Kurtosis3525.9813
Mean4760.4755
Median Absolute Deviation (MAD)541
Skewness58.49484
Sum18061244
Variance1.4361847 × 1010
MonotonicityNot monotonic
2024-11-11T15:55:01.221137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.4%
900 52
 
1.4%
1350 50
 
1.3%
1800 49
 
1.3%
1950 43
 
1.1%
3240 42
 
1.1%
2700 40
 
1.0%
2000 35
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1335) 3381
88.7%
ValueCountFrequency (%)
45 1
 
< 0.1%
50 5
0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
 
0.1%
61 1
 
< 0.1%
67 2
 
0.1%
70 1
 
< 0.1%
ValueCountFrequency (%)
7250000 1
< 0.1%
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%

areaWithType
Text

Distinct2434
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Memory size442.5 KiB
2024-11-11T15:55:01.444827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length53.82481
Min length12

Characters and Unicode

Total characters205234
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1920 ?
Unique (%)50.4%

Sample

1st rowPlot area 204(170.57 sq.m.)Built Up area: 200 sq.yards (167.23 sq.m.)Carpet area: 180 sq.yards (150.5 sq.m.)
2nd rowCarpet area: 1156 (107.4 sq.m.)
3rd rowPlot area 342(285.96 sq.m.)
4th rowSuper Built up area 1655(153.75 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1550 sq.ft. (144 sq.m.)
5th rowSuper Built up area 2093(194.45 sq.m.)
ValueCountFrequency (%)
area 5754
18.5%
sq.m 3791
12.2%
up 3079
 
9.9%
built 2358
 
7.6%
super 1893
 
6.1%
sq.ft 1778
 
5.7%
sq.m.)carpet 1213
 
3.9%
plot 748
 
2.4%
sq.m.)built 719
 
2.3%
carpet 710
 
2.3%
Other values (2918) 8995
29.0%
2024-11-11T15:55:01.780612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27225
 
13.3%
. 21006
 
10.2%
a 13589
 
6.6%
r 9728
 
4.7%
e 9574
 
4.7%
1 9427
 
4.6%
s 7811
 
3.8%
q 7657
 
3.7%
t 7532
 
3.7%
p 6899
 
3.4%
Other values (25) 84786
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85120
41.5%
Decimal Number 48505
23.6%
Space Separator 27225
 
13.3%
Other Punctuation 24119
 
11.8%
Uppercase Letter 8833
 
4.3%
Close Punctuation 5716
 
2.8%
Open Punctuation 5716
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13589
16.0%
r 9728
11.4%
e 9574
11.2%
s 7811
9.2%
q 7657
9.0%
t 7532
8.8%
p 6899
8.1%
u 6865
8.1%
m 5725
6.7%
l 3827
 
4.5%
Other values (5) 5913
6.9%
Decimal Number
ValueCountFrequency (%)
1 9427
19.4%
0 6886
14.2%
2 5838
12.0%
5 4849
10.0%
3 4045
8.3%
4 3821
7.9%
6 3776
7.8%
7 3347
 
6.9%
8 3279
 
6.8%
9 3237
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3079
34.9%
C 1927
21.8%
S 1893
21.4%
U 1186
 
13.4%
P 748
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 21006
87.1%
: 3113
 
12.9%
Space Separator
ValueCountFrequency (%)
27225
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5716
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111281
54.2%
Latin 93953
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13589
14.5%
r 9728
10.4%
e 9574
10.2%
s 7811
8.3%
q 7657
8.1%
t 7532
8.0%
p 6899
7.3%
u 6865
7.3%
m 5725
 
6.1%
l 3827
 
4.1%
Other values (10) 14746
15.7%
Common
ValueCountFrequency (%)
27225
24.5%
. 21006
18.9%
1 9427
 
8.5%
0 6886
 
6.2%
2 5838
 
5.2%
) 5716
 
5.1%
( 5716
 
5.1%
5 4849
 
4.4%
3 4045
 
3.6%
4 3821
 
3.4%
Other values (5) 16752
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27225
 
13.3%
. 21006
 
10.2%
a 13589
 
6.6%
r 9728
 
4.7%
e 9574
 
4.7%
1 9427
 
4.6%
s 7811
 
3.8%
q 7657
 
3.7%
t 7532
 
3.7%
p 6899
 
3.4%
Other values (25) 84786
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.380278
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:01.874121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.024677
Coefficient of variation (CV)0.59896761
Kurtosis44.128762
Mean3.380278
Median Absolute Deviation (MAD)1
Skewness4.7619989
Sum12889
Variance4.0993171
MonotonicityNot monotonic
2024-11-11T15:55:01.949616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1530
40.1%
2 977
25.6%
4 686
18.0%
5 222
 
5.8%
1 134
 
3.5%
6 82
 
2.2%
9 45
 
1.2%
8 32
 
0.8%
7 31
 
0.8%
12 26
 
0.7%
Other values (11) 48
 
1.3%
ValueCountFrequency (%)
1 134
 
3.5%
2 977
25.6%
3 1530
40.1%
4 686
18.0%
5 222
 
5.8%
6 82
 
2.2%
7 31
 
0.8%
8 32
 
0.8%
9 45
 
1.2%
10 21
 
0.6%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 26
0.7%

bathroom
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4222397
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:02.024370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum36
Range35
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0590823
Coefficient of variation (CV)0.60167683
Kurtosis41.954742
Mean3.4222397
Median Absolute Deviation (MAD)1
Skewness4.5223605
Sum13049
Variance4.2398201
MonotonicityNot monotonic
2024-11-11T15:55:02.100078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1116
29.3%
2 1095
28.7%
4 839
22.0%
5 301
 
7.9%
1 169
 
4.4%
6 120
 
3.1%
7 43
 
1.1%
9 42
 
1.1%
8 26
 
0.7%
12 21
 
0.6%
Other values (11) 41
 
1.1%
ValueCountFrequency (%)
1 169
 
4.4%
2 1095
28.7%
3 1116
29.3%
4 839
22.0%
5 301
 
7.9%
6 120
 
3.1%
7 43
 
1.1%
8 26
 
0.7%
9 42
 
1.1%
10 11
 
0.3%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 1
 
< 0.1%
13 4
 
0.1%
12 21
0.6%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size247.1 KiB
3+
1188 
3
1097 
2
925 
1
403 
No
200 

Length

Max length2
Median length1
Mean length1.3640178
Min length1

Characters and Unicode

Total characters5201
Distinct characters6
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row2
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3+ 1188
31.2%
3 1097
28.8%
2 925
24.3%
1 403
 
10.6%
No 200
 
5.2%

Length

2024-11-11T15:55:02.181946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:02.254616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2285
59.9%
2 925
24.3%
1 403
 
10.6%
no 200
 
5.2%

Most occurring characters

ValueCountFrequency (%)
3 2285
43.9%
+ 1188
22.8%
2 925
17.8%
1 403
 
7.7%
N 200
 
3.8%
o 200
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3613
69.5%
Math Symbol 1188
 
22.8%
Uppercase Letter 200
 
3.8%
Lowercase Letter 200
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2285
63.2%
2 925
25.6%
1 403
 
11.2%
Math Symbol
ValueCountFrequency (%)
+ 1188
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 200
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4801
92.3%
Latin 400
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2285
47.6%
+ 1188
24.7%
2 925
19.3%
1 403
 
8.4%
Latin
ValueCountFrequency (%)
N 200
50.0%
o 200
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2285
43.9%
+ 1188
22.8%
2 925
17.8%
1 403
 
7.7%
N 200
 
3.8%
o 200
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct44
Distinct (%)1.2%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.652847
Minimum0
Maximum51
Zeros134
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:02.339792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.9918518
Coefficient of variation (CV)0.90064476
Kurtosis4.7827548
Mean6.652847
Median Absolute Deviation (MAD)3
Skewness1.7489324
Sum25354
Variance35.902288
MonotonicityNot monotonic
2024-11-11T15:55:02.432139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 537
14.1%
2 533
14.0%
1 390
10.2%
4 327
 
8.6%
8 197
 
5.2%
6 184
 
4.8%
7 181
 
4.7%
10 180
 
4.7%
5 172
 
4.5%
9 162
 
4.2%
Other values (34) 948
24.9%
ValueCountFrequency (%)
0 134
 
3.5%
1 390
10.2%
2 533
14.0%
3 537
14.1%
4 327
8.6%
5 172
 
4.5%
6 184
 
4.8%
7 181
 
4.7%
8 197
 
5.2%
9 162
 
4.2%
ValueCountFrequency (%)
51 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
43 2
0.1%
41 1
< 0.1%
40 1
< 0.1%
39 2
0.1%
38 1
< 0.1%
35 2
0.1%
34 2
0.1%

facing
Categorical

High correlation 

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size262.3 KiB
na
1103 
north-east
488 
east
471 
north
290 
south
201 
Other values (12)
1260 

Length

Max length10
Median length5
Mean length5.4429583
Min length2

Characters and Unicode

Total characters20754
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-East
2nd rowna
3rd rowEast
4th rowsouth
5th rownorth-east

Common Values

ValueCountFrequency (%)
na 1103
28.9%
north-east 488
12.8%
east 471
12.4%
north 290
 
7.6%
south 201
 
5.3%
west 178
 
4.7%
East 176
 
4.6%
North-East 157
 
4.1%
north-west 155
 
4.1%
south-east 143
 
3.8%
Other values (7) 451
11.8%

Length

2024-11-11T15:55:02.523323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1103
28.9%
east 647
17.0%
north-east 645
16.9%
north 399
 
10.5%
west 250
 
6.6%
south 235
 
6.2%
north-west 199
 
5.2%
south-east 182
 
4.8%
south-west 153
 
4.0%

Most occurring characters

ValueCountFrequency (%)
t 3889
18.7%
a 2577
12.4%
s 2551
12.3%
n 2036
9.8%
o 1813
8.7%
h 1813
8.7%
e 1704
8.2%
r 1243
 
6.0%
- 1179
 
5.7%
u 570
 
2.7%
Other values (5) 1379
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18660
89.9%
Dash Punctuation 1179
 
5.7%
Uppercase Letter 915
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3889
20.8%
a 2577
13.8%
s 2551
13.7%
n 2036
10.9%
o 1813
9.7%
h 1813
9.7%
e 1704
9.1%
r 1243
 
6.7%
u 570
 
3.1%
w 464
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
E 372
40.7%
N 310
33.9%
W 138
 
15.1%
S 95
 
10.4%
Dash Punctuation
ValueCountFrequency (%)
- 1179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19575
94.3%
Common 1179
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3889
19.9%
a 2577
13.2%
s 2551
13.0%
n 2036
10.4%
o 1813
9.3%
h 1813
9.3%
e 1704
8.7%
r 1243
 
6.3%
u 570
 
2.9%
w 464
 
2.4%
Other values (4) 915
 
4.7%
Common
ValueCountFrequency (%)
- 1179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3889
18.7%
a 2577
12.4%
s 2551
12.3%
n 2036
9.8%
o 1813
8.7%
h 1813
8.7%
e 1704
8.2%
r 1243
 
6.0%
- 1179
 
5.7%
u 570
 
2.7%
Other values (5) 1379
 
6.6%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size291.7 KiB
Relatively New
1671 
New Property
612 
Moderately Old
592 
Old Property
338 
undefined
331 

Length

Max length18
Median length14
Mean length13.349856
Min length9

Characters and Unicode

Total characters50903
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Old
2nd rowundefined
3rd rowModerately Old
4th rowRelatively New
5th rowModerately Old

Common Values

ValueCountFrequency (%)
Relatively New 1671
43.8%
New Property 612
 
16.1%
Moderately Old 592
 
15.5%
Old Property 338
 
8.9%
undefined 331
 
8.7%
Under Construction 269
 
7.1%

Length

2024-11-11T15:55:02.603625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:02.676896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2283
31.3%
relatively 1671
22.9%
property 950
13.0%
old 930
12.7%
moderately 592
 
8.1%
undefined 331
 
4.5%
under 269
 
3.7%
construction 269
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 8690
17.1%
l 4864
 
9.6%
t 3751
 
7.4%
3482
 
6.8%
y 3213
 
6.3%
r 3030
 
6.0%
d 2453
 
4.8%
N 2283
 
4.5%
w 2283
 
4.5%
i 2271
 
4.5%
Other values (15) 14583
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40457
79.5%
Uppercase Letter 6964
 
13.7%
Space Separator 3482
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8690
21.5%
l 4864
12.0%
t 3751
9.3%
y 3213
 
7.9%
r 3030
 
7.5%
d 2453
 
6.1%
w 2283
 
5.6%
i 2271
 
5.6%
a 2263
 
5.6%
o 2080
 
5.1%
Other values (7) 5559
13.7%
Uppercase Letter
ValueCountFrequency (%)
N 2283
32.8%
R 1671
24.0%
P 950
13.6%
O 930
13.4%
M 592
 
8.5%
U 269
 
3.9%
C 269
 
3.9%
Space Separator
ValueCountFrequency (%)
3482
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47421
93.2%
Common 3482
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8690
18.3%
l 4864
 
10.3%
t 3751
 
7.9%
y 3213
 
6.8%
r 3030
 
6.4%
d 2453
 
5.2%
N 2283
 
4.8%
w 2283
 
4.8%
i 2271
 
4.8%
a 2263
 
4.8%
Other values (14) 12320
26.0%
Common
ValueCountFrequency (%)
3482
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8690
17.1%
l 4864
 
9.6%
t 3751
 
7.4%
3482
 
6.8%
y 3213
 
6.3%
r 3030
 
6.0%
d 2453
 
4.8%
N 2283
 
4.5%
w 2283
 
4.5%
i 2271
 
4.5%
Other values (15) 14583
28.6%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct599
Distinct (%)31.6%
Missing1920
Missing (%)50.4%
Infinite0
Infinite (%)0.0%
Mean1920.9152
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:02.774065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile750
Q11465
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)750

Descriptive statistics

Standard deviation765.40848
Coefficient of variation (CV)0.39846031
Kurtosis10.207098
Mean1920.9152
Median Absolute Deviation (MAD)372
Skewness1.8188209
Sum3636292.5
Variance585850.14
MonotonicityNot monotonic
2024-11-11T15:55:02.868824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.4%
Other values (589) 1652
43.3%
(Missing) 1920
50.4%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct677
Distinct (%)37.9%
Missing2028
Missing (%)53.2%
Infinite0
Infinite (%)0.0%
Mean2322.8821
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:02.957840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile215
Q11000
median1616
Q32367
95-th percentile4646
Maximum737147
Range737145
Interquartile range (IQR)1367

Descriptive statistics

Standard deviation17473.104
Coefficient of variation (CV)7.5221654
Kurtosis1756.2133
Mean2322.8821
Median Absolute Deviation (MAD)637.77
Skewness41.740224
Sum4146344.6
Variance3.0530935 × 108
MonotonicityNot monotonic
2024-11-11T15:55:03.053086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 42
 
1.1%
900 37
 
1.0%
3240 35
 
0.9%
1900 35
 
0.9%
2700 34
 
0.9%
1350 34
 
0.9%
1600 26
 
0.7%
2000 25
 
0.7%
1300 24
 
0.6%
1700 24
 
0.6%
Other values (667) 1469
38.5%
(Missing) 2028
53.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
45 1
 
< 0.1%
50 5
0.1%
52.5 1
 
< 0.1%
53 1
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
26000 1
 
< 0.1%
13500 1
 
< 0.1%
12000 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8260 1
 
< 0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct743
Distinct (%)38.6%
Missing1886
Missing (%)49.5%
Infinite0
Infinite (%)0.0%
Mean2489.0904
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:03.144916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile339.3
Q1822
median1280
Q31782
95-th percentile2941.6
Maximum607936
Range607921
Interquartile range (IQR)960

Descriptive statistics

Standard deviation22473.689
Coefficient of variation (CV)9.0288763
Kurtosis622.32119
Mean2489.0904
Median Absolute Deviation (MAD)480
Skewness24.687112
Sum4796477.2
Variance5.0506671 × 108
MonotonicityNot monotonic
2024-11-11T15:55:03.235948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 36
 
0.9%
1600 35
 
0.9%
1200 32
 
0.8%
1500 29
 
0.8%
1650 28
 
0.7%
1350 28
 
0.7%
900 24
 
0.6%
1000 23
 
0.6%
1300 23
 
0.6%
Other values (733) 1627
42.7%
(Missing) 1886
49.5%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 2
0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.8 KiB
0
3095 
1
718 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3813
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3095
81.2%
1 718
 
18.8%

Length

2024-11-11T15:55:03.318491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:03.381055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3095
81.2%
1 718
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 3095
81.2%
1 718
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3813
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3095
81.2%
1 718
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3095
81.2%
1 718
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3095
81.2%
1 718
 
18.8%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.8 KiB
0
2473 
1
1340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3813
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2473
64.9%
1 1340
35.1%

Length

2024-11-11T15:55:03.448143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:03.510771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2473
64.9%
1 1340
35.1%

Most occurring characters

ValueCountFrequency (%)
0 2473
64.9%
1 1340
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3813
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2473
64.9%
1 1340
35.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2473
64.9%
1 1340
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2473
64.9%
1 1340
35.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.8 KiB
0
3462 
1
351 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3813
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3462
90.8%
1 351
 
9.2%

Length

2024-11-11T15:55:03.578065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:03.640022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3462
90.8%
1 351
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3462
90.8%
1 351
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3813
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3462
90.8%
1 351
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3462
90.8%
1 351
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3462
90.8%
1 351
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.8 KiB
0
3140 
1
673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3813
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3140
82.3%
1 673
 
17.7%

Length

2024-11-11T15:55:03.707598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:03.769926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3140
82.3%
1 673
 
17.7%

Most occurring characters

ValueCountFrequency (%)
0 3140
82.3%
1 673
 
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3813
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3140
82.3%
1 673
 
17.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3140
82.3%
1 673
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3140
82.3%
1 673
 
17.7%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.8 KiB
0
3393 
1
420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3813
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3393
89.0%
1 420
 
11.0%

Length

2024-11-11T15:55:03.836349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:03.899291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3393
89.0%
1 420
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3393
89.0%
1 420
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3813
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3393
89.0%
1 420
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3393
89.0%
1 420
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3393
89.0%
1 420
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size245.8 KiB
0
2521 
2
1081 
1
 
211

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3813
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 2521
66.1%
2 1081
28.4%
1 211
 
5.5%

Length

2024-11-11T15:55:03.966073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-11T15:55:04.030994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2521
66.1%
2 1081
28.4%
1 211
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2521
66.1%
2 1081
28.4%
1 211
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3813
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2521
66.1%
2 1081
28.4%
1 211
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2521
66.1%
2 1081
28.4%
1 211
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2521
66.1%
2 1081
28.4%
1 211
 
5.5%

luxary_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.833464
Minimum0
Maximum174
Zeros525
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size59.6 KiB
2024-11-11T15:55:04.107850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129
median56
Q3108
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.269005
Coefficient of variation (CV)0.76280055
Kurtosis-0.85936255
Mean69.833464
Median Absolute Deviation (MAD)39
Skewness0.48502084
Sum266275
Variance2837.5869
MonotonicityNot monotonic
2024-11-11T15:55:04.202199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 525
 
13.8%
49 352
 
9.2%
174 195
 
5.1%
44 62
 
1.6%
165 56
 
1.5%
38 55
 
1.4%
72 52
 
1.4%
7 50
 
1.3%
42 48
 
1.3%
60 47
 
1.2%
Other values (151) 2371
62.2%
ValueCountFrequency (%)
0 525
13.8%
5 6
 
0.2%
6 6
 
0.2%
7 50
 
1.3%
8 33
 
0.9%
9 8
 
0.2%
12 9
 
0.2%
13 11
 
0.3%
14 13
 
0.3%
15 46
 
1.2%
ValueCountFrequency (%)
174 195
5.1%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 56
 
1.5%
161 3
 
0.1%
160 30
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2024-11-11T15:54:58.150164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:51.787266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.465710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.260838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.950943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.654316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.339929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.005530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.783278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.476960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.218161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:51.858661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.533854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.328722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.018048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.720468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.404760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.188774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.852635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.542036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.289422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:51.926179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.600980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.397393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.087492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.790217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.472309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.257751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.923545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.610658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.359575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:51.995722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.672489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.468413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.169640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.860139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.543179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.321202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.996468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.678418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.430526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.062992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.743522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.539317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.239183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.929887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.610203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.393699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.068799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.748202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.500944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.131647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.814007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.609221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.308528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.998490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.677768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.460780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.141419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.818958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.566854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.195944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.879730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.675256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.373394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.063909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.739186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.522477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.208310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.881934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.635774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.260068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.947077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.739183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.441476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.127958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.801784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.586543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.272329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.948266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.771324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.331100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.018088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.810624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.512675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.199805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.871309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.644603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.343036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.009057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.840563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:52.396516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.087915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:53.878947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:54.583548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.268555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:55.937049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:56.713369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:57.404087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:54:58.076859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-11T15:55:04.279186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxary_scoreotherspooja roompriceprice_per_sq_ftproperty typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2670.0770.1210.0000.0000.2480.1290.2130.2530.1120.1860.0970.0570.3880.2840.1480.1420.088
area0.0001.0000.0000.6820.6060.8300.8000.1060.1300.0430.2770.0000.0000.7470.1980.0190.0000.0000.0000.949
balcony0.2670.0001.0000.1600.1110.0000.0220.1760.0850.1820.2240.0760.1900.1360.0370.2180.4390.1370.1860.308
bathroom0.0770.6820.1601.0000.8530.4650.5900.1990.0090.1600.1830.0610.2620.7190.4070.4260.3030.1710.1550.819
bedRoom0.1210.6060.1110.8531.0000.3560.5570.242-0.0990.1320.0490.0560.2630.6700.4120.6090.1270.1950.1590.799
built_up_area0.0000.8300.0000.4650.3561.0000.9690.0000.1200.0870.3230.0000.0000.6060.1180.0000.0000.0000.0000.926
carpet_area0.0000.8000.0220.5900.5570.9691.0000.0000.1650.0000.2430.0160.0000.6110.1300.0000.0000.0000.0080.895
facing0.2480.1060.1760.1990.2420.0000.0001.0000.1430.2060.1850.0780.3300.1900.0870.8330.2970.2980.1930.093
floorNum0.1290.1300.0850.009-0.0990.1200.1650.1431.0000.0200.2520.0340.1000.017-0.1170.4950.0930.1100.0710.152
furnishing_type0.2130.0430.1820.1600.1320.0870.0000.2060.0201.0000.2540.0570.2200.1820.0000.0650.2790.1590.1510.137
luxary_score0.2530.2770.2240.1830.0490.3230.2430.1850.2520.2541.0000.1690.1930.2340.0640.3500.3590.2300.1920.226
others0.1120.0000.0760.0610.0560.0000.0160.0780.0340.0570.1691.0000.0370.0370.0160.0300.0000.1060.0380.088
pooja room0.1860.0000.1900.2620.2630.0000.0000.3300.1000.2200.1930.0371.0000.3310.0180.2400.2530.2980.3190.159
price0.0970.7470.1360.7190.6700.6060.6110.1900.0170.1820.2340.0370.3311.0000.7340.5060.3710.2980.2450.775
price_per_sq_ft0.0570.1980.0370.4070.4120.1180.1300.087-0.1170.0000.0640.0160.0180.7341.0000.2060.0410.0000.0300.292
property type0.3880.0190.2180.4260.6090.0000.0000.8330.4950.0650.3500.0300.2400.5060.2061.0000.0430.2320.1151.000
servant room0.2840.0000.4390.3030.1270.0000.0000.2970.0930.2790.3590.0000.2530.3710.0410.0431.0000.1570.1920.584
store room0.1480.0000.1370.1710.1950.0000.0000.2980.1100.1590.2300.1060.2980.2980.0000.2320.1571.0000.2180.043
study room0.1420.0000.1860.1550.1590.0000.0080.1930.0710.1510.1920.0380.3190.2450.0300.1150.1920.2181.0000.123
super_built_up_area0.0880.9490.3080.8190.7990.9260.8950.0930.1520.1370.2260.0880.1590.7750.2921.0000.5840.0430.1231.000

Missing values

2024-11-11T15:54:58.953888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-11T15:54:59.172954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-11T15:54:59.321553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property typesocietysectorpriceprice_per_sq_ftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxary_score
0houseindependentsector 314.2526235.01620.0Plot area 204(170.57 sq.m.)Built Up area: 200 sq.yards (167.23 sq.m.)Carpet area: 180 sq.yards (150.5 sq.m.)553+3.0North-EastModerately OldNaN200.0180.000110256
1houseindependentsector 11.8516003.01156.0Carpet area: 1156 (107.4 sq.m.)4323.0naundefinedNaNNaN1156.00000000
2houseindependentsector 5012.5040611.03078.0Plot area 342(285.96 sq.m.)6534.0EastModerately OldNaN3078.0NaN11010223
3flataipl zen residencessector 701.8210996.01655.0Super Built up area 1655(153.75 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1550 sq.ft. (144 sq.m.)33312.0southRelatively New1655.01600.01550.0000000108
4flatthe close northsector 502.5111992.02093.0Super Built up area 2093(194.45 sq.m.)33311.0north-eastModerately Old2093.0NaNNaN001102109
5flatsuncity vatsal valleysector 21.389650.01430.0Super Built up area 1430(132.85 sq.m.)3321.0eastRelatively New1430.0NaNNaN000002151
6flatm3m merlinsector 673.3914376.02358.0Super Built up area 2358(219.07 sq.m.)Built Up area: 2100 sq.ft. (195.1 sq.m.)Carpet area: 2000 sq.ft. (185.81 sq.m.)34314.0eastRelatively New2358.02100.02000.001000226
7flatcapital residences 360sector 701.517641.01976.0Super Built up area 1976(183.58 sq.m.)3337.0south-eastRelatively New1976.0NaNNaN01000049
8flatsignature the roseliasector 950.457908.0569.0Carpet area: 569 (52.86 sq.m.)2222.0eastNew PropertyNaNNaN569.000000231
9housedlf city phase 1sector 265.5030556.01800.0Plot area 200(167.23 sq.m.)4432.0North-EastModerately OldNaN1800.0NaN11010069
property typesocietysectorpriceprice_per_sq_ftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxary_score
3932flatambience creacionssector 223.9912912.03090.0Super Built up area 3090(287.07 sq.m.)353+3.0naNew Property3090.0NaNNaN010001102
3933houseindependentsector 534.0017778.02250.0Plot area 250(209.03 sq.m.)5523.0naRelatively NewNaN2250.0NaN10000023
3934flatvalley view estatesector 500.386089.0624.0Super Built up area 624(57.97 sq.m.)11111.0naModerately Old624.0NaNNaN00000042
3935flatsare green parcsector 920.856538.01300.0Built Up area: 1300 (120.77 sq.m.)32220.0naundefinedNaN1300.0NaN0000000
3936flatmicrotek greenburgsector 861.938446.02285.0Super Built up area 2285(212.28 sq.m.)34312.0eastRelatively New2285.0NaNNaN01000072
3937flatsignature the roseliasector 950.416194.0662.0Built Up area: 670 (62.25 sq.m.)Carpet area: 569.25 sq.ft. (52.89 sq.m.)22212.0northNew PropertyNaN670.0569.2510000290
3938flatsignature global parksector 1630.546388.0845.0Super Built up area 845.3(78.53 sq.m.)Carpet area: 528.3 sq.ft. (49.08 sq.m.)2234.0eastNew Property845.3NaN528.3000000044
3939housesector 14 rwasector 146.5020635.03150.0Plot area 350(292.64 sq.m.)443+2.0naModerately OldNaN3150.0NaN0000000
3940flatexperion the heartsongsector 1081.308344.01558.0Super Built up area 1758(163.32 sq.m.)Carpet area: 1558 sq.ft. (144.74 sq.m.)333+7.0south-westRelatively New1758.0NaN1558.00100000150
3941flatshree vardhman florasector 901.005128.01950.0Super Built up area 1950(181.16 sq.m.)3438.0north-eastNew Property1950.0NaNNaN01000095